Platform
- Spearheaded Learning Machines, Agno’s learning layer: 6 learning stores and 3 learning modes so agents and teams improve every run.
- Designed Agno’s sub-agent system: a coordinator splits complex tasks across specialists, each with its own isolated context.
- Architected per-user isolation across all 17 database backends, unlocking multi-tenant distributed workloads and caching. Access control
- Unified tool-result compression and history compaction into one context manager, so long-running agents stay on-task. Context engineering
- Took concurrent-request failures from 31% to 0% and cut latency and memory across the agent runtime.
Products
- Took Scout, Dash and Pal to production for enterprise teams - any model, deployed in the customer’s own cloud - with the one-command AgentOS templates.
Team
- Mentored and onboarded engineers, and cut CI time roughly in half by parallelizing the test suite. AI code review